EM-TECH brings together 10 participants from industry and academia to develop novel solutions to push the boundaries of electric machine technology for automotive traction, through: i) innovative direct and active cooling designs; ii) virtual sensing functionalities for the high-fidelity real-time estimation of the operating condition of the machine; iii) enhanced machine control, bringing reduced design and operating conservativeness enabled by ii); iv) electric gearing to provide enhanced operational flexibility and energy efficiency; v) digital twin based optimisation, embedding systematic consideration of Life Cycle Analysis and Life Cycle Costing aspects since the early design stages; and vi) adoption of recycled permanent magnets and circularity solutions. The proposed innovations will be implemented in new series of radial flux direct drive in-wheel motors characterised by so far unexplored levels of torque density (>150 Nm/litre, >50 Nm/kg), and on-board single stator double rotor type ironless axial flux machines providing power density and specific power levels in excess of 30 kW/litre and 10 kW/kg. The solutions will address both passenger car and van applications (continuous power levels of 50 kW - 120 kW), providing competitive costs (25%), and to >60% decrease of the rare earth content, including implementation of magnet recycling solutions. EM-TECH obtained the support of several car makers (AUDI and Changan UK), which will strengthen the exploitation strategy. EM-TECH will further directly contribute to the relevant European Destination and KSO C and A, by supporting the establishment of a European leadership in the sector of key digital, enabling and emerging technologies, and the development of the respective value chains.

Major disasters (e.g., Ponte Morandi, 2018) highlighted the critical condition of many European civil infrastructures due to inadequate maintenance, unforeseen operating conditions, and extended usage. Regular inspections of all types of infrastructures are essential to assess their health, guide maintenance and repair, and extend service life. SARAH adopts a human-centred approach to develop a safe-by-design integrated digital solution that supports maintenance and repair decision and planning, order, intervention and reporting. Leveraging metaverse technologies and egocentric augmented reality solutions, the system empowers infrastructure supervisors and intervention workers by providing real-time assistance, fostering skill development, and improving their Occupational Safety and Health. The solution, rooted in Human Factors: a/ identifies issues in civil infrastructures in a rapid, cost-effective and safe manner thanks to innovative sensors deployed on-site (corrosion, soil nail and anchor, structural crack) possibly via an unmanned aerial system (load up to 1kg); b/ fastly and accurately assesses the needs for repair (98 % of issues properly ranked) using a Digital Twin of the infrastructure able to run in real-time (reduced order, automatic continuous calibration) and proposes prioritised timely interventions, considering risk assessment using a Decision Support Engine (80% ad hoc correct advice for inspections); c/ launches extra inspections (either with autonomous aerial system or human), and provides remote support to the intervention workers (80% satisfaction, evaluated via interviews); d/ delivers certification report generated using generative AI, after intervention and repair (at least 2 types of reports generated). With this approach, SARAH offers a game changer for infrastructure inspection, maintenance and repair.

Focused on BEV architectures with distributed multiple wheel drives, and, specifically, in-wheel powertrains, HighScape will explore the feasibility of a family of highly efficient power electronics components and systems, and including integrated traction inverters, on-board chargers, DC/DC converters, and electric drives for auxiliaries and actuators. The proposed solutions will be assessed on test rigs and on two differently sized BEV prototypes. The project will result in: i) component integration with the incorporation of the WBG traction inverters within the in-wheel machines to achieve zero footprint of the electric powertrain on the sprung mass; the functional integration of the traction inverter with the on-board charger, and the incorporation of the latter and the DC/DC converters within the battery pack; and the implementation of multi-motor and fault-tolerant inverter solutions for the auxiliaries and chassis actuators; ii) novel solutions, including the implementation of reconfigurable winding topologies of the drive, as well as integrated and predictive thermal management at the vehicle level, with the adoption of phase changing materials within the power electronics components; iii) the achievement and demonstration of significantly higher levels of power density, specific power and energy efficiency for the resulting power electronics systems and related drives; iv) major cost reductions thanks to the dual use of parts, subsystem modularity, and model-based design to eliminate overengineering; and v) increased dependability and reliability of the power electronics systems, enabled by design and intelligent predictive health monitoring algorithms. Through HighScape, the participants will establish new knowledge and industrial leadership in key digital technologies, and, therefore, directly contribute to Europe’s Key Strategic Orientations as well as actively support the transformation towards zero tailpipe emission road mobility (2Zero).

In-wheel motors (IWMs) have become a mature technology that is well-suited for new user-centric electric vehicles (EVs). IWMs can be integrated in multi-functional and controllable modules consisting of the electric powertrain, friction brake and suspension/steering actuation. By combining several vehicle functionalities in a compact solution, the modules offer substantial opportunities to enhance the design and the operation of EVs. This is the starting point of the SmartCorners project. Using machine learning and AI, an adaptive multilayer control strategy will be implemented with historical and current data from the vehicle, its environment, and users, including relevant EV fleets. This approach will pave the way toward software-defined vehicles, ena-bling rightsizing, holistic optimisation, innovative fault mitigation and actuator allocation strategies as well as more efficient, adaptive, predictive, and personalised system operation. SmartCorners will bring a so far un-explored authority level over: i) vehicle design, through skateboard-like chassis configurations; ii) energy management aspects, covering pre-conditioning and predictive thermal management during EV operation; iii) comfort and functional aspects, in terms of user-centric cabin thermal management, and pre-emptive vehicle body control; and iv) dismantling process and recycling of the vehicle. The development and industrialization of the project outcomes will be accelerated by comprehensive and integrated simulation, design and validation methodologies to decrease development time and cost, and support the uptake of AI-based solutions. In con-clusion, SmartCorners will provide a significant competitive advantage of the European industry and contrib-ute to achieve key strategic orientations C and A of the EU Strategic Plan.

The CODE4EV project aims to accelerate the development of electric software-defined vehicles (SDVs) by establishing a collaborative development framework. This framework will support the design, production and operational phases of electric vehicles (EVs) by demonstrating its application through selected Use Cases relevant to emerging and future SDV architectures. The project key objectives include the elaboration of digital design tools and a trustworthy development methodology for electric SDVs, improving the efficiency and reliability of SDV architecture component sharing, and accelerating validation processes. The project also focuses on the implementation of a model-based design, the development of a symbolic ontology knowledge database, and the migration from rapid prototyping environments to automotive SW environments to improve development processes and compliance with industry standards. In addition, CODE4EV aims to provide multi-layered benefits throughout the design, production and operational phases of EVs. This includes methods for defining the SDV architecture, real-time runtime virtualisation approaches, and developing modular HW architectures to optimise data usage. The project Use Cases will demonstrate the implementation of the collaborative development framework, such as data-driven EV optimisation, health monitoring and predictive maintenance, and smart motion control. These Use Cases aim to demonstrate improvements in energy consumption, component life extension and overall vehicle performance. CODE4EV plans to develop virtual, hybrid and full-scale demonstrators of electric SDVs for different vehicle categories, focusing on efficient verification procedures and the evaluation of the scalability of the CODE4EV approach. These efforts aim to ensure compatibility and efficiency for a range of vehicle types, including heavy-duty trucks and L-class EVs, thereby making an important contribution to the promotion of zero-emission mobility solutions.